Deep learning is a technology used to cluster or classify objects or data. For example, computers cannot distinguish dogs and cats from photographs only. But a human can easily distinguish those two. To this end, a method called “machine learning” was devised. It is a technique to allow a computer to classify similar things among lots of data inputted thereto. When a photo of an animal similar to a dog is inputted, the computer will classify it as a dog photo.
There have already been many machine learning algorithms to classify data. For example, a decision tree, a Bayesian network, a support vector machine (SVM), an artificial neural network, etc. have been developed. The deep learning is a descendant of the artificial neural network.
Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problems of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
FIG. 1 is a drawing schematically illustrating a process of a general segmentation by using a CNN.
By referring to FIG. 1, according to a conventional lane detection method, a learning device receives an input image, instructs one or more multiple convolutional layers to generate at least one feature map by applying one or more multiple convolution operations and one or more non-linear operations, e.g., ReLU, to the input image, and then generates a segmentation result by instructing one or more deconvolutional layers to apply one or more deconvolution operations and SoftMax operations to the feature maps.
However, there is a problem that many of edges are missed in the process of encoding and decoding the input image so various methods have been provided to solve the problem and to reinforce the edges in the input image or its corresponding feature map. For example, Golnaz Ghiasi and Charless C. Fowlkes have suggested a method of the image segmentation using a Laplacian Pyramid by a paper called “Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation” (https://www.ics.uci.edu/˜fowlkes/papers/gf-eccv16.pdf). This method was adopted to extract the edges from a small-sized feature map, and to add information on the edges to a large-sized feature map. However, it is difficult to achieve a significant improvement because most of the information on the edges is lost.
It is also difficult to say that this method actually uses the Laplacian Pyramid since it does not use the concept of separating a range of high frequencies into predetermined bands, unlike the title of the paper. Further, this method has a problem that it does not detect the edges accurately as it uses randomly generated edges rather than originally existing edges.